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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
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A Semi-Supervised Multi-Region Segmentation Framework of Bladder Wall and Tumor with Wall-Enhanced Self-Supervised

Jie Wei1,2, Yao Zheng1, Dong Huang1,2

  • 1School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi'an 710032, China.

Bioengineering (Basel, Switzerland)
|January 8, 2025
PubMed
Summary

This study introduces a novel semi-supervised framework for segmenting bladder cancer and walls in MRI scans, improving accuracy with limited data. The method enhances bladder wall discrimination and achieves high segmentation performance, aiding clinical decisions.

Keywords:
bladder cancermagnetic resonance imagemulti-region segmentationself-supervised learningsemi-supervised learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Bladder cancer segmentation in MRI is vital for treatment decisions.
  • Limited high-quality annotated data hinders data-driven segmentation accuracy.
  • Distinguishing bladder wall and tumor is challenging for radiologists.

Purpose of the Study:

  • To develop a semi-supervised framework for bladder wall and tumor segmentation using limited annotated and unlabeled MRI data.
  • To enhance the discrimination of the bladder wall using self-supervised pre-training.
  • To improve the overall segmentation performance for bladder cancer.

Main Methods:

  • A semi-supervised multi-region framework incorporating wall-enhanced self-supervised pre-training.
  • Utilized limited annotated and unlabeled data for segmentation.
  • Introduced contrast consistency and reconstruction observation losses, adaptive learning rates, and post-processing techniques.

Main Results:

  • Achieved average Dice Similarity Coefficients (DSCs) of 0.8351 for the bladder wall and 0.9175 for the tumor.
  • Demonstrated effective reduction of segmentation artifacts outside the bladder.
  • Showcased improved clinical significance of segmentation results.

Conclusions:

  • The proposed semi-supervised framework effectively segments bladder walls and tumors in MRI.
  • The method leverages limited annotated data and unlabeled data for improved performance.
  • Results indicate enhanced clinical utility for bladder cancer diagnosis and treatment planning.